In processing a digital image, it is common to sharpen the image and enhance fine detail with sharpening algorithms. Typically, sharpening is performed by a convolution process (for example, see A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall: 1989, pp. 249-251). The process of unsharp masking is an example of a convolution-based sharpening process. For example, sharpening an image with unsharp masking can be described by the equation:s(x,y)=i(x,y)**b(x,y)+βf(i(x,y)−i(x,y)**b(x,y))  (0)where:                s(x,y)=output image with enhanced sharpness        i(x,y)=original input image        b(x,y)=lowpass filter        β=unsharp mask scale factor        f( )=fringe function        ** denotes two dimensional convolution        (x,y) denotes the xth row and the yth column of an image        
Typically, an unsharp image is generated by convolution of the image with a lowpass filter (i.e., the unsharp image is given by i(x,y)**b(x,y)). Next, the highpass, or fringe data is generated by subtracting the unsharp image from the original image (i.e., the highpass data is found with i(x,y)−i(x,y)**b(x,y)). This highpass data is then modified by either a scale factor β or a fringe function f( ) or both. Finally, the modified highpass data is summed with either the original image or the unsharp image to produce a sharpened image.
A similar sharpening effect can be achieved by modification of the image in the frequency domain (for example, the FFT domain) as is well known in the art of digital signal processing.
In U.S. Pat. No. 4,571,635 issued Feb. 18, 1996, Mahmoodi et al. teach a method of deriving an emphasis coefficient β that is used to scale the high frequency information of the digital image depending on the standard deviation of the image pixels within a neighborhood. In addition, in U.S. Pat. No. 5,081,692 issued Jan. 14, 1992, Kwon et al teach that emphasis coefficient β is based on a center weighted variance calculation. In U.S. Pat. No. 4,761,819 issued Aug. 2, 1988, Denison et al. describes a method where the gain of an unsharp mask is dependent on both a local variance calculation and a noise statistic.
While these methods do indeed sharpen the image while attempting to minimize noise enhancement, they do not correctly consider the expected noise from the imaging system and therefore do not provide optimum performance. For example, none of these methods account for the variation of expected imaging system noise due signal intensity and image color.
A need exists therefore for an improved method of predicting the noise in an image for use in an image sharpening algorithm.